Hi @maoquan-ms 🤗
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work through Papers with Code as yours got featured: https://paperswithcode.co/paper/2606.14066.
The paper page lets people discuss your paper and find related artifacts. I see you've already shared the model weights for FastContext in a collection on the Hugging Face Hub (https://huggingface.co/collections/microsoft/swe-fastcontext), which is awesome for visibility and reproducibility!
Would you also like to host the training trajectories/datasets you've mentioned on https://huggingface.co/datasets? Hosting them on Hugging Face will give the data more visibility and allow people to easily load it via the library:
from datasets import load_dataset
dataset = load_dataset("microsoft/fastcontext-trajectories")If you're interested, you can find a guide here: https://huggingface.co/docs/datasets/loading. Besides that, there's the dataset viewer which allows people to quickly explore the data in the browser.
After they are uploaded, we can also link both the datasets and the models to the paper page (read here) so the community can discover your work more easily. You can also claim the paper as yours on Hugging Face to show it on your public profile.
Let me know if you're interested or need any guidance!
Kind regards,
Niels
Hi @maoquan-ms 🤗
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work through Papers with Code as yours got featured: https://paperswithcode.co/paper/2606.14066.
The paper page lets people discuss your paper and find related artifacts. I see you've already shared the model weights for FastContext in a collection on the Hugging Face Hub (https://huggingface.co/collections/microsoft/swe-fastcontext), which is awesome for visibility and reproducibility!
Would you also like to host the training trajectories/datasets you've mentioned on https://huggingface.co/datasets? Hosting them on Hugging Face will give the data more visibility and allow people to easily load it via the library:
If you're interested, you can find a guide here: https://huggingface.co/docs/datasets/loading. Besides that, there's the dataset viewer which allows people to quickly explore the data in the browser.
After they are uploaded, we can also link both the datasets and the models to the paper page (read here) so the community can discover your work more easily. You can also claim the paper as yours on Hugging Face to show it on your public profile.
Let me know if you're interested or need any guidance!
Kind regards,
Niels